{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-10T06:35:14.800461Z",
     "start_time": "2025-04-10T06:35:14.796530Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T06:38:54.172164Z",
     "start_time": "2025-04-10T06:35:21.970146Z"
    }
   },
   "cell_type": "code",
   "source": [
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)"
   ],
   "id": "4a1c96fdb9fa90a2",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\Anaconda\\Anacondalocation\\Lib\\site-packages\\sklearn\\datasets\\_openml.py:109: UserWarning: A network error occurred while downloading https://api.openml.org/api/v1/json/data/list/data_name/mnist_784/limit/2/data_version/1. Retrying...\n",
      "  warn(\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T06:42:06.884164Z",
     "start_time": "2025-04-10T06:42:04.202067Z"
    }
   },
   "cell_type": "code",
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "id": "8ac833247cbfb5a4",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T06:42:28.179299Z",
     "start_time": "2025-04-10T06:42:26.767928Z"
    }
   },
   "cell_type": "code",
   "source": "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)",
   "id": "76f3a195aee15194",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T06:44:24.338060Z",
     "start_time": "2025-04-10T06:44:24.333734Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ],
   "id": "fcb8ef9bd42b91be",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T06:45:06.858722Z",
     "start_time": "2025-04-10T06:44:52.942481Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train, y_train)"
   ],
   "id": "ab5c671957412e18",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T06:46:26.547244Z",
     "start_time": "2025-04-10T06:46:26.509262Z"
    }
   },
   "cell_type": "code",
   "source": "y_pred = model.predict(X_test)",
   "id": "ac7dc5394c148037",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T06:46:46.854626Z",
     "start_time": "2025-04-10T06:46:46.793681Z"
    }
   },
   "cell_type": "code",
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)\n"
   ],
   "id": "1b0b6e6e2a81e25d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9155714285714286\n",
      "Precision: 0.9153349249252525\n",
      "Recall: 0.9155714285714286\n",
      "F1-score: 0.915357939521866\n",
      "Confusion Matrix:\n",
      " [[1284    1   11    0    1   12   22    4    6    2]\n",
      " [   0 1555    6    9    3    5    1    4   14    3]\n",
      " [   5   20 1232   20   13   13   21   16   28   12]\n",
      " [   7    8   37 1272    1   41    7   22   20   18]\n",
      " [   6    2   10    4 1187    5   13    9    8   51]\n",
      " [  11   11    8   43   12 1114   21    1   36   16]\n",
      " [   6    4   18    1   17   21 1320    3    6    0]\n",
      " [   4    4   23    4   13    6    0 1414    2   33]\n",
      " [  10   33   16   44    6   45   14    9 1162   18]\n",
      " [   7    9    7   15   37    5    0   50   12 1278]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96      1343\n",
      "           1       0.94      0.97      0.96      1600\n",
      "           2       0.90      0.89      0.90      1380\n",
      "           3       0.90      0.89      0.89      1433\n",
      "           4       0.92      0.92      0.92      1295\n",
      "           5       0.88      0.88      0.88      1273\n",
      "           6       0.93      0.95      0.94      1396\n",
      "           7       0.92      0.94      0.93      1503\n",
      "           8       0.90      0.86      0.88      1357\n",
      "           9       0.89      0.90      0.90      1420\n",
      "\n",
      "    accuracy                           0.92     14000\n",
      "   macro avg       0.91      0.91      0.91     14000\n",
      "weighted avg       0.92      0.92      0.92     14000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-10T06:47:05.159274Z",
     "start_time": "2025-04-10T06:47:04.561006Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"Logistic Regression - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "id": "cb696122c14033",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "87c417722fc2e532"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
